Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction

Discovery and optimization of new catalysts can be potentially accelerated by efficient data analysis using machine-learning (ML). In this paper, we record the process of searching for additives in the electrochemical deposition of Cu catalysts for CO2 reduction (CO2RR) using ML, which includes three iterative cycles: experimental test; ML analysis; prediction and redesign. Cu catalysts are known for CO2RR to obtain a range of products including C-1 (CO, HCOOH, CH4, CH3OH) and C2+ (C2H4, C2H6, C2H5OH, C3H7OH). Subtle changes in morphology and surface structure of the catalysts caused by additives in catalyst preparation can lead to dramatic shifts in CO2RR selectivity. After several ML cycles, we obtained catalysts selective for CO, HCOOH, and C2+ products. This catalyst discovery process highlights the potential of ML to accelerate material development by efficiently extracting information from a limited number of experimental data.

Automated summary

The study utilizes machine-learning to search for additives in the electrochemical deposition of Cu catalysts for CO2 reduction, showing that additives can cause subtle changes in catalyst morphology and surface structure, leading to significant shifts in CO2RR selectivity. Through ML cycles, catalysts selective for CO, HCOOH, and C2+ products were obtained.